Volatility forecasting: evidence from a fractional integrated asymmetric power ARCH skewed-t model

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Power arch modelling of the volatility of emerging equity markets (2007, methodology): “Degiannakis (2004) use a skewed Student-t version of the APARCH model to estimate value at risk for three US stocks and three European markets (France, UK, Germany).”
Frontiers in VaR forecasting and backtesting (2016, background): “Some authors also suggest incorporating long-memory in the volatility when computing the VaR; see, for example, Degiannakis (2004), Caporin (2008) and Dark (2010).”
Forecasting global stock market implied volatility indices (2018, methodology): “…studies have concentrated their attention on the use of models which are variants of GARCH models (see, inter alia, Bollerslev et al., 1994; Degiannakis, 2004; Hansen and Lunde, 2005), stochastic volatility models (see, among others, Deo, 2006; Yu, 2012) or realized volatility models…”
Forecasting oil price realized volatility using information channels from other asset classes (2017, methodology): “2 See, Bollerslev et al. (1992), Andersen et al. (2003, 2005), Degiannakis (2004), Hansen and Lunde (2005), Angelidis and Degiannakis (2008), Fuertes et al. (2009), Frijns et al. (2010) among others.”